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Jungfraujoch/common/CUDAWrapper.cpp
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leonarski_fandFilip Leonarski 19b135186d
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v1.0.0-rc.175 (#85)
* Rugnux: Processes a collection live while jfjoch_broker writes it (`rugnux <master.h5> <broker URL>`).
* Rugnux: Processing starts on the first images and confirms its choices once all are in, so analysis can begin during collection.
* Rugnux: Faster processing, especially on servers with several GPUs and CPU sockets and on computers without a GPU (`--no-gpu`, macOS).
* Rugnux: Uses much less GPU memory, so large data sets stay on the GPU of smaller cards, and a data set too large for the card is processed on the CPU instead.
* Rugnux: On macOS, Bragg integration runs on the Apple GPU.
* Rugnux: Better handling of twinning and pseudo-symmetry, with the twin hypothesis written as `<prefix>_twin_<SG>.mtz`, and a wrong `--model` no longer confirms the data or decides the hand.
* Rugnux: `--model` accepts predicted (AlphaFold) models and falls back to molecular replacement when the model is not placed as a rigid body (experimental `--mr` forces it), settling space group, hand and twinning and writing a different answer as `<prefix>_mr.mtz`.
* Rugnux: Molecular replacement is faster and settles the space group in more cases with several copies in the asymmetric unit.
* Rugnux: Deposition-ready mmCIF, a richer report, and numbered steps on the terminal with the full log in `<prefix>_log.txt`; `_P1.mtz`, `--estimate-beam-center` and `--beam-center-search` are removed.
* Rugnux: The beam-stop shadow is found correctly on tilted detectors.
* Rugnux: Stills files that record a stationary goniometer axis get the default stills geometry refinement.
* jfjoch_viewer: "Analyze dataset" uses the same defaults as a bare `rugnux <file>`, and shows the results of processed CBF, marCCD and SMV sweeps.
* jfjoch_viewer: The Files panel refreshes on F5 and every few seconds, and the plot type and other selections stay put during live collection.
* jfjoch_writer: Memory stays bounded and old data files are closed efficiently on long runs, any failure ends the run in an error state the broker is told about, and the master file appears only after its data files; the ZeroMQ pusher logs when a stalled writer makes it drop images.
* Fixes: The TCP image pusher no longer blocks the broker's receiver, GPU out-of-memory failures stop with a clear message, and the XDS plugin masks module and chip edges.

---------

Co-authored-by: Filip Leonarski <jungfrau@filips-mac-mini.home>
Reviewed-on: #85
Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>
2026-10-16 15:34:43 +02:00

165 lines
4.1 KiB
C++

// SPDX-FileCopyrightText: 2024 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only
#include "CUDAWrapper.h"
#include <atomic>
#include <condition_variable>
#include <mutex>
// Build-independent: the CUDA build gets get_gpu_names() from CUDAWrapper.cu, the CPU-only build from
// the stub below, and this collapses whichever list came back. Four identical cards read better as
// "4x <name>" than as the same name four times, and a mixed machine keeps one group per model.
std::string get_gpu_description() {
const auto names = get_gpu_names();
std::string out;
for (size_t i = 0; i < names.size();) {
size_t n = 1;
while (i + n < names.size() && names[i + n] == names[i])
n++;
if (!out.empty())
out += ", ";
if (n > 1)
out += std::to_string(n) + "x ";
out += names[i];
i += n;
}
return out;
}
namespace {
// How many probe passes (GPUWorkBeside) and main-line image passes (GPUImagePass) are running, for a
// thread to wait until none of one kind is.
std::mutex gpu_work_m;
std::condition_variable gpu_work_changed;
int gpu_work_beside_n = 0, gpu_image_passes_n = 0, gpu_merges_n = 0;
// A card too small for a probe pass beside the main line's image pass, or beside another probe pass:
// measured on a 16 Mpx detector, two of them together held 8.6 GB. On such a card a probe pass waits,
// before it starts, for every other one and for the image passes to end, and an image pass for the
// probe passes - so they take turns, and only when each runs depends on the card, never what it
// computes. None waits once it is running, so nothing waits in a circle.
constexpr size_t TAKE_TURNS_BELOW_BYTES = 12'000'000'000ull;
bool take_turns() {
static const bool small = get_gpu_count() > 0 && get_gpu_total_memory(0) < TAKE_TURNS_BELOW_BYTES;
return small;
}
void remove(int &n) {
{
std::lock_guard lock(gpu_work_m);
--n;
}
gpu_work_changed.notify_all();
}
}
bool gpu_takes_turns() {
return take_turns();
}
GPUWorkBeside::GPUWorkBeside(bool after_merges) {
std::unique_lock lock(gpu_work_m);
if (take_turns())
gpu_work_changed.wait(lock, [after_merges] {
return gpu_image_passes_n == 0 && gpu_work_beside_n == 0 && (!after_merges || gpu_merges_n == 0);
});
++gpu_work_beside_n;
}
GPUWorkBeside::~GPUWorkBeside() {
remove(gpu_work_beside_n);
}
bool wait_for_gpu_work_beside(std::chrono::seconds timeout) {
std::unique_lock lock(gpu_work_m);
return gpu_work_changed.wait_for(lock, timeout, [] { return gpu_work_beside_n == 0; });
}
GPUImagePass::GPUImagePass() {
std::unique_lock lock(gpu_work_m);
if (take_turns())
gpu_work_changed.wait(lock, [] { return gpu_work_beside_n == 0; });
++gpu_image_passes_n;
}
GPUImagePass::~GPUImagePass() {
remove(gpu_image_passes_n);
}
GPUMerge::GPUMerge() {
std::lock_guard lock(gpu_work_m);
++gpu_merges_n;
}
GPUMerge::~GPUMerge() {
remove(gpu_merges_n);
}
void wait_for_gpu_image_passes() {
std::unique_lock lock(gpu_work_m);
gpu_work_changed.wait(lock, [] { return gpu_image_passes_n == 0; });
}
#ifndef JFJOCH_USE_CUDA
int32_t get_gpu_count() {
return 0;
}
namespace {
std::atomic<bool> gpus_hidden{false};
}
void hide_gpus() {
gpus_hidden = true;
}
bool gpus_are_hidden() {
return gpus_hidden;
}
std::vector<std::string> get_gpu_names() {
return {};
}
size_t get_gpu_total_memory(int32_t) {
return 0;
}
bool get_gpu_free_memory(size_t &, size_t &) {
return false;
}
void set_gpu(int32_t dev_id) {}
bool gpu_direct_host_read() {
return false;
}
void pin_gpu() {}
void pin_gpu(int32_t dev_id) {}
ScopedGpuPin::ScopedGpuPin(int32_t) {}
ScopedGpuPin::~ScopedGpuPin() {}
void enable_gpu_numa_binding() {}
std::vector<int> get_gpu_numa_nodes() { return {}; }
void set_gpu_blocking_sync() {}
void create_gpu_contexts(int32_t) {}
void release_gpu_contexts() {}
void cuda_clear_error() {}
void cuda_throw_if_context_lost() {}
#endif